The modern era of data-driven system discovery started in earnest with the work of [1]}, [2]} on symbolic regression. Since then, probabilistic methods [3]}, [4]}, [5]}, [6]} and deep neural networks [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]} have proven to be effective tools for modeling high-dimensional complex dynamical systems.
However, it is the seminal work of [14]} on the Sparse Identification of Nonlinear Dynamics (SINDy) framework that serves as the foundation for our approach. SINDy casts system identification as a sparse regression problem over a large set of nonlinear library functions to find the fewest active terms which accurately reconstruct the system dynamics. Such a technique is especially useful for finding highly interpretable models, and performs well even with limited training data (in particular, much less than what a neural network would require). Its success has inspired a large number of extensions and variants tailored for more specific problems [15]}, [16]}, [17]}, [18]}.
